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+# 2026-02-25 Session Notes
+
+## VISION.md Gap Analysis and Enhancement
+
+Nicholai identified a potential gap between the predictive-memory-scorer.md technical plan and the high-level vision document, asking whether new concepts should be elevated to VISION.md.
+
+After reviewing both documents, Claude identified one significant conceptual gap: **learned adaptation**. VISION.md discusses memory and coherence as static capabilities—"Signet gives agents coherence" and "a mind that persists"—but doesn't explain that the system improves over time by learning from interaction patterns.
+
+The predictive-memory-scorer.md captures this elegantly: a per-user model trained on interaction history that learns what makes memories relevant, what temporal patterns matter, and which memories actually helped. Critically, this model trains locally with no cloud infrastructure or shared weights.
+
+Proposed addition to VISION.md's continuity section:
+"Signet doesn't just remember — it learns what to remember. A model unique to each user, trained on their own interaction patterns, that gets sharper the longer you use it. No cloud. No shared weights. Your patterns, running locally."
+
+This reinforces the decentralization thesis already in VISION.md while grounding it in a concrete, time-improving capability. The addition was accepted and applied.
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